Abstract 3047: Association of 16S rRNA fecal microbiome data with colorectal cancer risk: A pooled analysis
Authors: Madhawa Gunathilake, Jeongseon Kim
Journal: Cancer Research (2023)

Abstract Changes in the fecal microbial composition may have a potential role in colorectal carcinogenesis and could provide insights into an individual’s risk of developing colorectal cancer (CRC). Although the previous studies observed the association between fecal microbiome and CRC risk, the evidence from large-scale population-based studies are lacking and the causality is still yet to be established. To address this gap, we conducted a microbiome-wide association study (MWAS) to identify CRC associated unique microbial markers among Koreans. Three publicly available datasets were downloaded from NCBI Sequence Read Archive under accession numbers PRJNA747730 (70 CRC cases), PRJNA797640 (200 CRC cases), and PRJNA644479 (179 healthy controls). Additionally, 295 CRC patients were recruited from the Center for Colorectal Cancer, National Cancer Center (NCC) hospital, Korea. Total study population comprised with 565 CRC cases and 179 healthy controls. Fecal samples of NCC cases were collected and 16S rRNA gene sequencing was performed. Forward sequence reads from all four datasets were used for quality control and taxonomic annotation using QIIME2 pipeline. Gaussian graphical models were applied to derive a microbial network and microbial communities were detected within the network using R package “linkcomm”. Microbial community-specific scores were calculated for each cluster and the association between community-specific score and CRC risk was observed using unconditional logistic regression models. Microbial communities were evaluated using machine learning models to identify CRC specific bacterial markers. There was a significant divergence of the microbial composition between CRC patients and controls (PERMANOVA p=0.001). Faecalibacterium and Megamonas were enriched in healthy controls whereas Escherichia-Shigella was enriched in CRC cases. Out of 24 communities, community 6: odds ratio (OR=2.68), 95% confidence intervals (CI=1.18-6.09, p-trend=0.006), 7 (OR=4.33, 95% CI=1.86-10.05, p-trend=0.001), and 11 (OR=7.28, 95% CI=3.35-15.81) were positively associated while community 19 (OR=0.34, 95% CI=0.15-0.78, p-trend=0.015) was negatively associated with CRC risk. Random forest showed the best performance to predict CRC risk based on 10 most important microbial communities (AUC=0.97). Community 20 with five genera had highest predictive ability (AUC=0.89) whereas Christensenellaceae_R_7 genus was identified as a unique microbial marker to distinguish CRC patients from healthy controls (AUC=0.85). This MWAS could help to identify Christensenellaceae_R_7 as the unique marker to distinguish CRC patients from healthy controls. Thus, it could work as a promising indicator for CRC diagnosis among Koreans specifically implementing in CRC screening programs. Citation Format: Madhawa Gunathilake, Jeongseon Kim. Association of 16S rRNA fecal microbiome data with colorectal cancer risk: A pooled analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3047.